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characteristic polynomial
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(Definition)
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Let $A$ be a $n \times n$ matrix over some field $k$ The characteristic polynomial $p_A(x)$ of $A$ in an indeterminate $x$ is defined by the determinant:
$$ p_A(x):=\det(A-x I) = \left|\begin{matrix} a_{11}-x & a_{12} & \cdots & a_{1n} \\ a_{21} & a_{22}-x & \cdots & a_{2n} \\ \vdots & \vdots & \ddots & \vdots \\ a_{n1} & a_{n2} & \cdots & a_{nn}-x \end{matrix} \right|$$
Remarks
- The polynomial $p_A(x)$ is an $n$ -degree polynomial over $k$
- If $A$ and $B$ are similar matrices, then $p_A(x)=p_B(x)$ because \begin{eqnarray*} p_A(x) &=& \det(A-xI) = \det(P^{-1}BP-xI) \\ &=& \det(P^{-1}BP-P^{-1}xIP) = \det(P^{-1})\det(B-xI)\det(P) \\ &=& \det(P)^{-1}\det(B-xI)\det(P)=\det(B-xI) = p_B(x) \end{eqnarray*}for some invertible matrix $P$
- The characteristic equation of $A$ is the equation $p_A(x)=0$ and the solutions to which are the eigenvalues of $A$
Now, let $T$ be a linear operator on a vector space $V$ of dimension $n<\infty$ Let $\alpha$ and $\beta$ be any two ordered bases for $V$ Then we may form the matrices $[T]_{\alpha}$ and $[T]_{\beta}$ The two matrix representations of $T$ are similar matrices, related by a change of bases matrix. Therefore, by the second remark above, we define the characteristic polynomial of $T$ denoted by $p_T(x)$ in the indeterminate $x$ by $$p_T(x):=p_{[T]_{\alpha}}(x).$$ The characteristic equation of $T$ is defined accordingly.
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"characteristic polynomial" is owned by CWoo. [ full author list (2) | owner history (1) ]
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See Also: equation
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characteristic equation |
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Cross-references: change of bases, matrix representations, ordered bases, dimension, vector space, linear operator, eigenvalues, solutions, equation, invertible, similar matrices, polynomial, determinant, indeterminate, field, matrix
There are 42 references to this entry.
This is version 7 of characteristic polynomial, born on 2002-02-05, modified 2007-12-15.
Object id is 1838, canonical name is CharacteristicEquation.
Accessed 17960 times total.
Classification:
| AMS MSC: | 15A18 (Linear and multilinear algebra; matrix theory :: Eigenvalues, singular values, and eigenvectors) |
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Pending Errata and Addenda
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